Traditionally CER has focused primarily on the average effects across broad populations. However, the effectiveness of interventions with respect to risk and/or benefit often varies by patient subgroups. Recent advancement of science and technology has led to the discovery of many biological and genetic markers associated with disease outcomes and treatment responses. These new markers combined with traditional clinical assessments hold great potential for identifying subgroups of patients who are most likely to benefit or are at high risk for toxicity from a particular therapy and thus may lead to personalized or tailored medicine. This project will develop statistical approaches to personalized medicine in CER. The methods can be used to guide and tailor the treatment or disease screening strategies for individual patients. These methods will enhance future CER and improve the quality of public health by providing the foundation for identifying the most effective clinical options for each individual patient. The specific aims of the proposal are: 1. To develop methods for assessing treatment effects at an individual level using data from randomized clinical trials with: (a) a single outcome, and (b) multi-dimensional outcomes that quantify both risks and benefits. We will develop systematic statistical procedures to identify future patients that would benefit from a new therapy vs. for example, the standard care. 2. To develop and apply stochastic models governing the early detection of disease to colon and prostate cancers. We will develop optimal screening examination strategies as a function of age and risk status. The screening strategies will involve risk-based recommendations rather than fixed-time recommendations. We will also investigate upper age limits for ending screening. 3. To develop and evaluate the patient-level incremental value of new diagnostic and prognostic modalities. We will develop quantitative methods for assessing how the incremental value of new predictive modalities may vary across sub-populations and for identifying sub-populations that benefit the most or the least from the new modalities using data from clinical trials or observational studies. 4. To develop methods to compare the effectiveness of treatments implemented in different studies. We will also develop patient-specific treatment selection strategies in this setting. The proposal is submitted by leading researchers with complimentary but integrated expertise in CER research from the Department of Biostatistics at the Harvard School of Public Health (HSPH) which provides a well- established infrastructure and environment for methodological development in CER. The researchers also have strong ties to prominent clinical trial networks (e.g., AIDS Clinical Trials Group and the Eastern Cooperative Oncology Group) and other data sources that can be utilized to apply the developed methods, putting HSPH in a unique position to ensure the success of the proposal. PUBLIC HEALTH RELEVANCE: We will develop novel statistical methods for personalized medicine in comparative effectiveness. These methods will allow more robust evidence-based decisions in clinical practice that are tailored to individual patients based on their personal characteristics, so that the best clinical decisions are made for individual patients and the efficiency in public health practice is optimized.